Microsensing
2024-04-11
library(readr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combineFirst Dataset, 4/10/24
Analyze dataset from 4/10/24, for the gels made on 4/8/24. There is an n = 3 for each thickness, with 3 gels of 200 um thickness (“thin”) and 3 gels of 1000 um thickness (“thick”). Three profiles were taken per gel, at a light intensity of PAR” 90 umol/m^2/s and a flow rate of 0.5 cm/s. Three additional profiles were taken per gel in the dark after 20 minutes of dark adaptation. The profiles were taken in the middle of the hydrogel, in the same position for all 6 profiles per gel.
Plotting profiles
files <- list.files(path = "./Polymerized040824_Measured041024", pattern = "\\.csv$", full.names = TRUE)
plot_list <- list()
for (file in files) {
file_name <- tools::file_path_sans_ext(basename(file))
file_data <- read_csv(paste(file),show_col_types = FALSE) %>%
mutate(Replicate = as.factor(Replicate)) %>%
rename("Oxygen (µmol/L)" = `A FireSting Ch1 (µmol/L)`)
plot <- ggplot(data = file_data, aes(y = `Oxygen (µmol/L)`, x = `Depth (µm)`,
color = `Condition`, shape = `Replicate`)) +
geom_point() + geom_line() +
coord_flip() + scale_x_reverse() + ylim(190, 295) +
theme_bw() + ggtitle(file_name) +
annotate("text", x = -1900, y = 295, label = paste("Start:", file_data$Time[1]), vjust = 1, hjust = 1, size = 3) +
annotate("text", x = -1800, y = 295, label = paste("End:", file_data$Time[nrow(file_data)]), vjust = 1, hjust = 1, size = 3) +
theme(legend.position = "bottom")
#print(plot)
plot_list[[file_name]] <- plot
}## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`arranged_plots <- arrangeGrob(grobs = plot_list, ncol = 3)
gridExtra::grid.arrange(arranged_plots)
ggsave("Plots/Polymerized040824_Measured041024_plots.pdf", arranged_plots, width = 16, height = 9, units = "in", device = "pdf")Notes about calculating flux
Wangpraseurt et al 2022: For each profile, the diffusive O2 flux was calculated using Fick’s first law of diffusion as described previously[31] (using a diffusion coefficient of DO2 = 2.2417 × 10−5). The effective DBL thickness δe as the intersection between the extrapolation of the linear part of the O2 slope within the boundary layer that intercepts with the O2 concentration of the bulk medium was calculated.[75]
75 - B. B. Jørgensen, N. P. Revsbech, Limnol. Oceanogr.1985, 30, 111.
31 - D. Wangpraseurt, S. You, F. Azam, G. Jacucci, O. Gaidarenko, M. Hildebrand, M. Kühl, A. G. Smith, M. P. Davey, A. Smith, D. D. Deheyn, S. Chen, S. Vignolini, Nat. Commun. 2020, 11, 1748.
“The diffusive O2 flux was calculated via Fick’s first law of diffusion for a water temperature = 25 °C and salinity = 30 using a molecular diffusion coefficient for O2 = 2.255 × 10−5 cm2 s−1(2). Gross photosynthesis was estimated via the light-dark shift method36. A flow chamber set-up provided slow laminar flow (flow rate = 0.5 cm s−1) and a fiber-optic halogen lamp (Schott KL2500, Schott, Germany) provided white light at defined levels of incident irradiance (400–700 nm) (0, 110, 220, and 1200 µmol photons m−2 s−1)2. Photosynthesis-irradiance curves were fitted to an exponential function37.”
Attempt to calculate flux
For the thick ones I am calculating the layer as at -250
# Get list of files for 1000um gels in the directory
files <- list.files(path = "./Polymerized040824_Measured041024", pattern = "^Gel040824_1000um_\\d\\.csv$")
# Create an empty dataframe to store results
results_df_1000um <- data.frame(
File = character(),
Scaffold_Thickness = character(),
Replicate = character(),
Flux = numeric(),
END_DBL = numeric(),
Oxygen_Surface = numeric(),
Oxygen_End_DBL = numeric()
)
# Loop over each file
for (file in files) {
# Read the file
data <- read_csv(paste0("./Polymerized040824_Measured041024/",file), show_col_types = FALSE) %>%
mutate(Replicate = as.factor(Replicate)) %>%
rename("Oxygen (µmol/L)" = `A FireSting Ch1 (µmol/L)`)
# Loop over each replicate
for (replicate in unique(data$Replicate)) {
# Filter data for the current replicate and condition
oxygen_concentration <- data %>%
filter(Replicate == replicate & Condition == "Light", `Depth (µm)` > -300) %>%
pull(`Oxygen (µmol/L)`)
depth <- data %>%
filter(Replicate == replicate & Condition == "Light", `Depth (µm)` > -300) %>%
pull(`Depth (µm)`)
# Fit a linear regression model to estimate the slope within the diffusive boundary layer
boundary_layer_lm <- lm(oxygen_concentration ~ depth)
slope <- coef(boundary_layer_lm)[[2]]
# Calculate flux using Fick’s first law of diffusion
DO2 <- 2.2417e-5 # Diffusion coefficient of oxygen
flux <- -DO2 * slope
# Add the result to the dataframe
results_df_1000um <- rbind(
results_df_1000um,
data.frame(
File = file,
Scaffold_Thickness = "1000 um",
Replicate = replicate,
Flux = flux,
END_DBL = min(depth),
Oxygen_Surface = max(oxygen_concentration),
Oxygen_End_DBL = min(oxygen_concentration)
)
)
}
}## New names:
## New names:
## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`results_df_1000um## File Scaffold_Thickness Replicate Flux END_DBL
## 1 Gel040824_1000um_4.csv 1000 um 1 -7.701213e-06 -250
## 2 Gel040824_1000um_4.csv 1000 um 2 -6.662409e-06 -250
## 3 Gel040824_1000um_4.csv 1000 um 3 -7.801359e-06 -250
## 4 Gel040824_1000um_5.csv 1000 um 1 -5.981432e-06 -250
## 5 Gel040824_1000um_5.csv 1000 um 2 -5.574327e-06 -250
## 6 Gel040824_1000um_5.csv 1000 um 3 -6.463321e-06 -250
## 7 Gel040824_1000um_6.csv 1000 um 1 -4.645738e-06 -250
## 8 Gel040824_1000um_6.csv 1000 um 2 -5.009059e-06 -250
## 9 Gel040824_1000um_6.csv 1000 um 3 -5.224788e-06 -250
## Oxygen_Surface Oxygen_End_DBL
## 1 293.493 214.610
## 2 285.615 212.969
## 3 292.447 209.878
## 4 281.456 214.343
## 5 282.833 223.194
## 6 283.175 211.752
## 7 264.643 213.406
## 8 269.245 213.521
## 9 272.468 215.985For the thinner ones, I am calculating with the boundary later at -400
# Get list of files for 200um gels in the directory
files <- list.files(path = "./Polymerized040824_Measured041024", pattern = "^Gel040824_200um_\\d\\.csv$")
# Create an empty dataframe to store results
results_df_200um <- data.frame(
File = character(),
Scaffold_Thickness = character(),
Replicate = character(),
Flux = numeric(),
END_DBL = numeric(),
Oxygen_Surface = numeric(),
Oxygen_End_DBL = numeric()
)
# Loop over each file
for (file in files) {
# Read the file
data <- read_csv(paste0("./Polymerized040824_Measured041024/",file), show_col_types = FALSE) %>%
mutate(Replicate = as.factor(Replicate)) %>%
rename("Oxygen (µmol/L)" = `A FireSting Ch1 (µmol/L)`)
# Loop over each replicate
for (replicate in unique(data$Replicate)) {
# Filter data for the current replicate and condition
oxygen_concentration <- data %>%
filter(Replicate == replicate & Condition == "Light", `Depth (µm)` > -450) %>%
pull(`Oxygen (µmol/L)`)
depth <- data %>%
filter(Replicate == replicate & Condition == "Light", `Depth (µm)` > -450) %>%
pull(`Depth (µm)`)
# Fit a linear regression model to estimate the slope within the diffusive boundary layer
boundary_layer_lm <- lm(oxygen_concentration ~ depth)
slope <- coef(boundary_layer_lm)[[2]]
# Calculate flux using Fick’s first law of diffusion
DO2 <- 2.2417e-5 # Diffusion coefficient of oxygen
flux <- -DO2 * slope
# Add the result to the dataframe
results_df_200um <- rbind(
results_df_200um,
data.frame(
File = file,
Scaffold_Thickness = "200 um",
Replicate = replicate,
Flux = flux,
END_DBL = min(depth),
Oxygen_Surface = max(oxygen_concentration),
Oxygen_End_DBL = min(oxygen_concentration)
)
)
}
}## New names:
## New names:
## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`results_df_200um## File Scaffold_Thickness Replicate Flux END_DBL
## 1 Gel040824_200um_4.csv 200 um 1 -1.848513e-06 -400
## 2 Gel040824_200um_4.csv 200 um 2 -1.999933e-06 -400
## 3 Gel040824_200um_4.csv 200 um 3 -1.833038e-06 -400
## 4 Gel040824_200um_5.csv 200 um 1 -1.499294e-06 -400
## 5 Gel040824_200um_5.csv 200 um 2 -1.423450e-06 -400
## 6 Gel040824_200um_5.csv 200 um 3 -1.639520e-06 -400
## 7 Gel040824_200um_6.csv 200 um 1 -1.447600e-06 -400
## 8 Gel040824_200um_6.csv 200 um 2 -1.388449e-06 -400
## 9 Gel040824_200um_6.csv 200 um 3 -1.516301e-06 -400
## Oxygen_Surface Oxygen_End_DBL
## 1 235.138 204.172
## 2 237.367 204.110
## 3 235.725 204.696
## 4 230.437 204.200
## 5 229.684 204.580
## 6 232.367 204.267
## 7 231.559 206.548
## 8 229.721 205.829
## 9 230.814 206.309Plotting
Full_results <- rbind(results_df_1000um, results_df_200um)
Full_results## File Scaffold_Thickness Replicate Flux END_DBL
## 1 Gel040824_1000um_4.csv 1000 um 1 -7.701213e-06 -250
## 2 Gel040824_1000um_4.csv 1000 um 2 -6.662409e-06 -250
## 3 Gel040824_1000um_4.csv 1000 um 3 -7.801359e-06 -250
## 4 Gel040824_1000um_5.csv 1000 um 1 -5.981432e-06 -250
## 5 Gel040824_1000um_5.csv 1000 um 2 -5.574327e-06 -250
## 6 Gel040824_1000um_5.csv 1000 um 3 -6.463321e-06 -250
## 7 Gel040824_1000um_6.csv 1000 um 1 -4.645738e-06 -250
## 8 Gel040824_1000um_6.csv 1000 um 2 -5.009059e-06 -250
## 9 Gel040824_1000um_6.csv 1000 um 3 -5.224788e-06 -250
## 10 Gel040824_200um_4.csv 200 um 1 -1.848513e-06 -400
## 11 Gel040824_200um_4.csv 200 um 2 -1.999933e-06 -400
## 12 Gel040824_200um_4.csv 200 um 3 -1.833038e-06 -400
## 13 Gel040824_200um_5.csv 200 um 1 -1.499294e-06 -400
## 14 Gel040824_200um_5.csv 200 um 2 -1.423450e-06 -400
## 15 Gel040824_200um_5.csv 200 um 3 -1.639520e-06 -400
## 16 Gel040824_200um_6.csv 200 um 1 -1.447600e-06 -400
## 17 Gel040824_200um_6.csv 200 um 2 -1.388449e-06 -400
## 18 Gel040824_200um_6.csv 200 um 3 -1.516301e-06 -400
## Oxygen_Surface Oxygen_End_DBL
## 1 293.493 214.610
## 2 285.615 212.969
## 3 292.447 209.878
## 4 281.456 214.343
## 5 282.833 223.194
## 6 283.175 211.752
## 7 264.643 213.406
## 8 269.245 213.521
## 9 272.468 215.985
## 10 235.138 204.172
## 11 237.367 204.110
## 12 235.725 204.696
## 13 230.437 204.200
## 14 229.684 204.580
## 15 232.367 204.267
## 16 231.559 206.548
## 17 229.721 205.829
## 18 230.814 206.309mean_flux <- Full_results %>%
group_by(Scaffold_Thickness) %>%
summarize(mean_flux = mean(Flux))
# Plotting the flux values for each thickness
ggplot(Full_results, aes(x = Scaffold_Thickness, y = Flux)) +
geom_boxplot() + geom_point(aes(color = File), position = position_jitter(width = 0.05)) +
geom_text(data = mean_flux, aes(label = paste("Mean Flux = \n", sprintf("%.2e", mean_flux)), y = mean_flux-.0000012), vjust = 0.1, position = position_nudge(x = 0.25)) +
labs(x = "Thickness (µm)", y = "Flux") +
ggtitle("Flux Comparison between Thicknesses,\n same volumetric density (4/10/24)")
# Running ANOVA
anova_result <- aov(Flux ~ Scaffold_Thickness, data = Full_results)
summary(anova_result)## Df Sum Sq Mean Sq F value Pr(>F)
## Scaffold_Thickness 1 9.098e-11 9.098e-11 136.6 3.01e-09 ***
## Residuals 16 1.065e-11 6.700e-13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# What about for oxygen surface concentration?
mean_Oxygen_Surface <- Full_results %>%
group_by(Scaffold_Thickness) %>%
summarize(mean_Oxygen_Surface = mean(Oxygen_Surface))
# Plotting the surface O2 values for each thickness
ggplot(Full_results, aes(x = Scaffold_Thickness, y = Oxygen_Surface)) +
geom_boxplot() + geom_point(aes(color = File), position = position_jitter(width = 0.05)) +
geom_text(data = mean_Oxygen_Surface, aes(label = paste("Mean Surface Oxygen = \n", round(mean_Oxygen_Surface, 1)), y = mean_Oxygen_Surface-25), vjust = 0.1, position = position_nudge(x = 0.0)) +
labs(x = "Thickness (µm)", y = "Oxygen Surface Concentration (µmol/L)") +
ggtitle("Oxygen Surface Concentration (µmol/L) Comparison between Thicknesses")
# Running ANOVA
anova_result <- aov(Oxygen_Surface ~ Scaffold_Thickness, data = Full_results)
summary(anova_result)## Df Sum Sq Mean Sq F value Pr(>F)
## Scaffold_Thickness 1 10395 10395 193.7 2.33e-10 ***
## Residuals 16 858 54
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Using segmented linear regression, segmented R package
library(segmented)## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## select## Loading required package: nlme##
## Attaching package: 'nlme'## The following object is masked from 'package:dplyr':
##
## collapseresults_df_1000um_segmented <- data.frame(
File = character(),
Scaffold_Thickness = character(),
Replicate = character(),
slope = numeric(),
Flux = numeric(),
Breakpoint = numeric(),
Oxygen_Surface = numeric(),
Oxygen_breakpoint = numeric(),
stringsAsFactors = FALSE
)
# Function to perform segmented regression and extract slope after breakpoint
perform_segmented_regression <- function(oxygen_concentration, depth) {
# Fit segmented regression model
fit <- segmented(lm(oxygen_concentration ~ depth), seg.Z = ~depth, psi = -250)
# Extract the breakpoint(s)
breakpoint <- fit$psi[, 2]
# Find the index of the datapoint nearest to the breakpoint
breakpoint_index <- which.min(abs(depth - breakpoint))
# Extract the oxygen concentration at the breakpoint
Oxygen_breakpoint <- oxygen_concentration[breakpoint_index]
# Extract the oxygen concentration at depth = 0
Oxygen_Surface <- oxygen_concentration[which(depth == 0)]
# Extract the slope after the breakpoint for each segment
lm_fit <- lm(oxygen_concentration ~ depth,
data = data.frame(depth = depth[breakpoint_index:length(depth)],
oxygen_concentration = oxygen_concentration[breakpoint_index:length(oxygen_concentration)]))
slope <- coef(lm_fit)["depth"]
# Calculate fitted values for segmented regression lines
fitted_values <- fitted(fit, newdata = data.frame(depth = depth))
# Create dataframe with depth and fitted oxygen concentration values
fitted_data <- data.frame("Depth" = depth, "Oxygen" = fitted_values)
return(list(
slope = slope,
breakpoint = breakpoint,
Oxygen_breakpoint = Oxygen_breakpoint,
Oxygen_Surface = Oxygen_Surface,
fitted_data = fitted_data
))
}
# Get list of CSV file names with "1000" in the title
file_list <- list.files(path = "./Polymerized040824_Measured041024", pattern = "1000.*\\.csv$", full.names = TRUE)
# Loop over files
for (file_path in file_list) {
# Read data from file
data <- read_csv(file_path, show_col_types = FALSE) %>%
mutate(Replicate = as.factor(Replicate)) %>%
rename("Oxygen (µmol/L)" = `A FireSting Ch1 (µmol/L)`) %>%
arrange(`Depth (µm)`)
# Loop over replicates
for (replicate_id in unique(data$Replicate)) {
# Subset data for the current replicate
replicate_data <- data %>%
filter(Replicate == replicate_id & Condition == "Light")
# Extract oxygen concentration and depth
oxygen_concentration <- replicate_data %>% pull(`Oxygen (µmol/L)`)
depth <- replicate_data %>% pull(`Depth (µm)`)
# Perform segmented regression
regression_results <- perform_segmented_regression(oxygen_concentration, depth)
# Plot data and segmented regression lines
p <- ggplot(replicate_data, aes(x = `Depth (µm)`, y = `Oxygen (µmol/L)`)) +
geom_point() +
geom_line(data = regression_results$fitted_data, aes(x = `Depth`, y = `Oxygen`), color = "red") +
labs(title = paste("Replicate", replicate_id), x = "Depth (µm)", y = "Oxygen (µmol/L)")
print(p)
# Calculate flux using Fick’s first law of diffusion
DO2 <- 2.2417e-5 # Diffusion coefficient of oxygen
flux <- -DO2 * regression_results$slope
# Store results in dataframe
results_df_1000um_segmented <- bind_rows(results_df_1000um_segmented, data.frame(
File = basename(file_path),
Scaffold_Thickness = "1000 um",
Replicate = replicate_id,
slope = regression_results$slope,
Flux = flux,
Breakpoint = regression_results$breakpoint,
Oxygen_Surface = regression_results$Oxygen_Surface,
Oxygen_breakpoint = regression_results$Oxygen_breakpoint,
stringsAsFactors = FALSE
))
}
}## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
# Print the results dataframe
print(results_df_1000um_segmented)## File Scaffold_Thickness Replicate slope
## depth...1 Gel040824_1000um_4.csv 1000 um 1 0.4163000
## depth...2 Gel040824_1000um_4.csv 1000 um 2 0.2972034
## depth...3 Gel040824_1000um_4.csv 1000 um 3 0.3480109
## depth...4 Gel040824_1000um_5.csv 1000 um 1 0.3158300
## depth...5 Gel040824_1000um_5.csv 1000 um 2 0.2252707
## depth...6 Gel040824_1000um_5.csv 1000 um 3 0.3405020
## depth...7 Gel040824_1000um_6.csv 1000 um 1 0.3064920
## depth...8 Gel040824_1000um_6.csv 1000 um 2 0.2625240
## depth...9 Gel040824_1000um_6.csv 1000 um 3 0.2755740
## Flux Breakpoint Oxygen_Surface Oxygen_breakpoint
## depth...1 -9.332197e-06 -186.4981 293.493 214.610
## depth...2 -6.662409e-06 -228.7611 285.615 212.969
## depth...3 -7.801359e-06 -226.2641 292.447 209.878
## depth...4 -7.079961e-06 -207.0086 281.456 221.030
## depth...5 -5.049894e-06 -276.5534 282.833 215.546
## depth...6 -7.633033e-06 -176.8002 283.175 216.541
## depth...7 -6.870631e-06 -162.7789 264.643 219.004
## depth...8 -5.885001e-06 -178.6104 269.245 217.059
## depth...9 -6.177542e-06 -207.1288 272.468 218.221print(results_df_1000um)## File Scaffold_Thickness Replicate Flux END_DBL
## 1 Gel040824_1000um_4.csv 1000 um 1 -7.701213e-06 -250
## 2 Gel040824_1000um_4.csv 1000 um 2 -6.662409e-06 -250
## 3 Gel040824_1000um_4.csv 1000 um 3 -7.801359e-06 -250
## 4 Gel040824_1000um_5.csv 1000 um 1 -5.981432e-06 -250
## 5 Gel040824_1000um_5.csv 1000 um 2 -5.574327e-06 -250
## 6 Gel040824_1000um_5.csv 1000 um 3 -6.463321e-06 -250
## 7 Gel040824_1000um_6.csv 1000 um 1 -4.645738e-06 -250
## 8 Gel040824_1000um_6.csv 1000 um 2 -5.009059e-06 -250
## 9 Gel040824_1000um_6.csv 1000 um 3 -5.224788e-06 -250
## Oxygen_Surface Oxygen_End_DBL
## 1 293.493 214.610
## 2 285.615 212.969
## 3 292.447 209.878
## 4 281.456 214.343
## 5 282.833 223.194
## 6 283.175 211.752
## 7 264.643 213.406
## 8 269.245 213.521
## 9 272.468 215.985results_df_200um_segmented <- data.frame(
File = character(),
Scaffold_Thickness = character(),
Replicate = character(),
slope = numeric(),
Flux = numeric(),
Breakpoint = numeric(),
Oxygen_Surface = numeric(),
Oxygen_breakpoint = numeric(),
stringsAsFactors = FALSE
)
# Function to perform segmented regression and extract slope after breakpoint
perform_segmented_regression <- function(oxygen_concentration, depth) {
# Fit segmented regression model
fit <- segmented(lm(oxygen_concentration ~ depth), seg.Z = ~depth, psi = -400)
# Extract the breakpoint(s)
breakpoint <- fit$psi[, 2]
# Find the index of the datapoint nearest to the breakpoint
breakpoint_index <- which.min(abs(depth - breakpoint))
# Extract the oxygen concentration at the breakpoint
Oxygen_breakpoint <- oxygen_concentration[breakpoint_index]
# Extract the oxygen concentration at depth = 0
Oxygen_Surface <- oxygen_concentration[which(depth == 0)]
# Extract the slope after the breakpoint for each segment
lm_fit <- lm(oxygen_concentration ~ depth,
data = data.frame(depth = depth[breakpoint_index:length(depth)],
oxygen_concentration = oxygen_concentration[breakpoint_index:length(oxygen_concentration)]))
slope <- coef(lm_fit)["depth"]
# Calculate fitted values for segmented regression lines
fitted_values <- fitted(fit, newdata = data.frame(depth = depth))
# Create dataframe with depth and fitted oxygen concentration values
fitted_data <- data.frame("Depth" = depth, "Oxygen" = fitted_values)
return(list(
slope = slope,
breakpoint = breakpoint,
Oxygen_breakpoint = Oxygen_breakpoint,
Oxygen_Surface = Oxygen_Surface,
fitted_data = fitted_data
))
}
# Get list of CSV file names with "200" in the title
file_list <- list.files(path = "./Polymerized040824_Measured041024", pattern = "200.*\\.csv$", full.names = TRUE)
# Loop over files
for (file_path in file_list) {
# Read data from file
data <- read_csv(file_path, show_col_types = FALSE) %>%
mutate(Replicate = as.factor(Replicate)) %>%
rename("Oxygen (µmol/L)" = `A FireSting Ch1 (µmol/L)`) %>%
arrange(`Depth (µm)`)
# Loop over replicates
for (replicate_id in unique(data$Replicate)) {
# Subset data for the current replicate
replicate_data <- data %>%
filter(Replicate == replicate_id & Condition == "Light")
# Extract oxygen concentration and depth
oxygen_concentration <- replicate_data %>% pull(`Oxygen (µmol/L)`)
depth <- replicate_data %>% pull(`Depth (µm)`)
# Perform segmented regression
regression_results <- perform_segmented_regression(oxygen_concentration, depth)
# Plot data and segmented regression lines
p <- ggplot(replicate_data, aes(x = `Depth (µm)`, y = `Oxygen (µmol/L)`)) +
geom_point() +
geom_line(data = regression_results$fitted_data, aes(x = `Depth`, y = `Oxygen`), color = "red") +
labs(title = paste("Replicate", replicate_id), x = "Depth (µm)", y = "Oxygen (µmol/L)")
print(p)
# Calculate flux using Fick’s first law of diffusion
DO2 <- 2.2417e-5 # Diffusion coefficient of oxygen
flux <- -DO2 * regression_results$slope
# Store results in dataframe
results_df_200um_segmented <- bind_rows(results_df_200um_segmented, data.frame(
File = basename(file_path),
Scaffold_Thickness = "200 um",
Replicate = replicate_id,
slope = regression_results$slope,
Flux = flux,
Breakpoint = regression_results$breakpoint,
Oxygen_Surface = regression_results$Oxygen_Surface,
Oxygen_breakpoint = regression_results$Oxygen_breakpoint,
stringsAsFactors = FALSE
))
}
}## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
# Print the results dataframe
print(results_df_200um_segmented)## File Scaffold_Thickness Replicate slope
## depth...1 Gel040824_200um_4.csv 200 um 1 0.10182714
## depth...2 Gel040824_200um_4.csv 200 um 2 0.12417314
## depth...3 Gel040824_200um_4.csv 200 um 3 0.11439429
## depth...4 Gel040824_200um_5.csv 200 um 1 0.09876400
## depth...5 Gel040824_200um_5.csv 200 um 2 0.09298171
## depth...6 Gel040824_200um_5.csv 200 um 3 0.10329143
## depth...7 Gel040824_200um_6.csv 200 um 1 0.09034171
## depth...8 Gel040824_200um_6.csv 200 um 2 0.06573738
## depth...9 Gel040824_200um_6.csv 200 um 3 0.08116286
## Flux Breakpoint Oxygen_Surface Oxygen_breakpoint
## depth...1 -2.282659e-06 -278.3708 235.138 206.914
## depth...2 -2.783589e-06 -269.4554 237.367 208.720
## depth...3 -2.564377e-06 -269.4545 235.725 208.342
## depth...4 -2.213993e-06 -236.2531 230.437 206.340
## depth...5 -2.084371e-06 -237.5620 229.684 207.066
## depth...6 -2.315484e-06 -266.7288 232.367 207.491
## depth...7 -2.025190e-06 -274.4906 231.559 209.808
## depth...8 -1.473635e-06 -340.0699 229.721 207.371
## depth...9 -1.819428e-06 -310.8676 230.814 207.816print(results_df_200um)## File Scaffold_Thickness Replicate Flux END_DBL
## 1 Gel040824_200um_4.csv 200 um 1 -1.848513e-06 -400
## 2 Gel040824_200um_4.csv 200 um 2 -1.999933e-06 -400
## 3 Gel040824_200um_4.csv 200 um 3 -1.833038e-06 -400
## 4 Gel040824_200um_5.csv 200 um 1 -1.499294e-06 -400
## 5 Gel040824_200um_5.csv 200 um 2 -1.423450e-06 -400
## 6 Gel040824_200um_5.csv 200 um 3 -1.639520e-06 -400
## 7 Gel040824_200um_6.csv 200 um 1 -1.447600e-06 -400
## 8 Gel040824_200um_6.csv 200 um 2 -1.388449e-06 -400
## 9 Gel040824_200um_6.csv 200 um 3 -1.516301e-06 -400
## Oxygen_Surface Oxygen_End_DBL
## 1 235.138 204.172
## 2 237.367 204.110
## 3 235.725 204.696
## 4 230.437 204.200
## 5 229.684 204.580
## 6 232.367 204.267
## 7 231.559 206.548
## 8 229.721 205.829
## 9 230.814 206.309Full_results_segmented <- rbind(results_df_1000um_segmented, results_df_200um_segmented)
Full_results_segmented## File Scaffold_Thickness Replicate slope
## depth...1 Gel040824_1000um_4.csv 1000 um 1 0.41630000
## depth...2 Gel040824_1000um_4.csv 1000 um 2 0.29720343
## depth...3 Gel040824_1000um_4.csv 1000 um 3 0.34801086
## depth...4 Gel040824_1000um_5.csv 1000 um 1 0.31583000
## depth...5 Gel040824_1000um_5.csv 1000 um 2 0.22527071
## depth...6 Gel040824_1000um_5.csv 1000 um 3 0.34050200
## depth...7 Gel040824_1000um_6.csv 1000 um 1 0.30649200
## depth...8 Gel040824_1000um_6.csv 1000 um 2 0.26252400
## depth...9 Gel040824_1000um_6.csv 1000 um 3 0.27557400
## depth...11 Gel040824_200um_4.csv 200 um 1 0.10182714
## depth...21 Gel040824_200um_4.csv 200 um 2 0.12417314
## depth...31 Gel040824_200um_4.csv 200 um 3 0.11439429
## depth...41 Gel040824_200um_5.csv 200 um 1 0.09876400
## depth...51 Gel040824_200um_5.csv 200 um 2 0.09298171
## depth...61 Gel040824_200um_5.csv 200 um 3 0.10329143
## depth...71 Gel040824_200um_6.csv 200 um 1 0.09034171
## depth...81 Gel040824_200um_6.csv 200 um 2 0.06573738
## depth...91 Gel040824_200um_6.csv 200 um 3 0.08116286
## Flux Breakpoint Oxygen_Surface Oxygen_breakpoint
## depth...1 -9.332197e-06 -186.4981 293.493 214.610
## depth...2 -6.662409e-06 -228.7611 285.615 212.969
## depth...3 -7.801359e-06 -226.2641 292.447 209.878
## depth...4 -7.079961e-06 -207.0086 281.456 221.030
## depth...5 -5.049894e-06 -276.5534 282.833 215.546
## depth...6 -7.633033e-06 -176.8002 283.175 216.541
## depth...7 -6.870631e-06 -162.7789 264.643 219.004
## depth...8 -5.885001e-06 -178.6104 269.245 217.059
## depth...9 -6.177542e-06 -207.1288 272.468 218.221
## depth...11 -2.282659e-06 -278.3708 235.138 206.914
## depth...21 -2.783589e-06 -269.4554 237.367 208.720
## depth...31 -2.564377e-06 -269.4545 235.725 208.342
## depth...41 -2.213993e-06 -236.2531 230.437 206.340
## depth...51 -2.084371e-06 -237.5620 229.684 207.066
## depth...61 -2.315484e-06 -266.7288 232.367 207.491
## depth...71 -2.025190e-06 -274.4906 231.559 209.808
## depth...81 -1.473635e-06 -340.0699 229.721 207.371
## depth...91 -1.819428e-06 -310.8676 230.814 207.816mean_flux <- Full_results_segmented %>%
group_by(Scaffold_Thickness) %>%
summarize(mean_flux = mean(Flux))
# Plotting the flux values for each thickness
flux_plot <- ggplot(Full_results_segmented, aes(x = Scaffold_Thickness, y = Flux)) +
geom_boxplot() + geom_point(aes(color = File), position = position_jitter(width = 0.05)) +
geom_text(data = mean_flux, aes(label = paste("Mean Flux = \n", sprintf("%.2e", mean_flux)), y = mean_flux-.000002), vjust = 0.1, position = position_nudge(x = 0.25)) +
labs(x = "Thickness (µm)", y = "Flux") +
ggtitle("Flux Comparison between Thicknesses,\n same volumetric density (4/10/24)\n Segmented Regression")
flux_plot
ggsave("Plots/Polymerized040824_Measured041024_Flux_plot.pdf", flux_plot, device = "pdf")## Saving 7 x 5 in image# Running ANOVA
anova_result <- aov(Flux ~ Scaffold_Thickness, data = Full_results_segmented)
summary(anova_result)## Df Sum Sq Mean Sq F value Pr(>F)
## Scaffold_Thickness 1 1.024e-10 1.024e-10 121.2 7.1e-09 ***
## Residuals 16 1.352e-11 8.400e-13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# What about for oxygen surface concentration?
mean_Oxygen_Surface <- Full_results_segmented %>%
group_by(Scaffold_Thickness) %>%
summarize(mean_Oxygen_Surface = mean(Oxygen_Surface))
# Plotting the surface O2 values for each thickness
ggplot(Full_results_segmented, aes(x = Scaffold_Thickness, y = Oxygen_Surface)) +
geom_boxplot() + geom_point(aes(color = File), position = position_jitter(width = 0.05)) +
geom_text(data = mean_Oxygen_Surface, aes(label = paste("Mean Surface Oxygen = \n", round(mean_Oxygen_Surface, 1)), y = mean_Oxygen_Surface-25), vjust = 0.1, position = position_nudge(x = 0.0)) +
labs(x = "Thickness (µm)", y = "Oxygen Surface Concentration (µmol/L)") +
ggtitle("Oxygen Surface Concentration (µmol/L) Comparison between Thicknesses")
# Running ANOVA
anova_result <- aov(Oxygen_Surface ~ Scaffold_Thickness, data = Full_results_segmented)
summary(anova_result)## Df Sum Sq Mean Sq F value Pr(>F)
## Scaffold_Thickness 1 10395 10395 193.7 2.33e-10 ***
## Residuals 16 858 54
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plotting profiles with DBL
files <- list.files(path = "./Polymerized040824_Measured041024", pattern = "\\.csv$", full.names = TRUE)
plot_list <- list()
for (file in files) {
file_name <- tools::file_path_sans_ext(basename(file))
file_data <- read_csv(paste(file),show_col_types = FALSE) %>%
mutate(Replicate = as.factor(Replicate)) %>%
rename("Oxygen (µmol/L)" = `A FireSting Ch1 (µmol/L)`)
# Extract the DBL depth for the current file
dbl_depth <- Full_results_segmented$Breakpoint[gsub("\\.csv$", "", Full_results_segmented$File) == file_name]
plot <- ggplot(data = file_data, aes(y = `Oxygen (µmol/L)`, x = `Depth (µm)`,
color = `Condition`, shape = `Replicate`)) +
geom_point() + geom_line() +
coord_flip() + scale_x_reverse() + ylim(190, 295) + xlim(0, -750) +
theme_bw() + ggtitle(file_name) +
annotate("text", x = -650, y = 295, label = paste("Start:", file_data$Time[1]), vjust = 1, hjust = 1, size = 3) +
annotate("text", x = -550, y = 295, label = paste("End:", file_data$Time[nrow(file_data)]), vjust = 1, hjust = 1, size = 3) +
geom_vline(xintercept = dbl_depth, linetype = "dashed") + # Add DBL depth line
theme(legend.position = "bottom")
#print(plot)
plot_list[[file_name]] <- plot
}## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`arranged_plots <- arrangeGrob(grobs = plot_list, ncol = 3)## Warning: Removed 144 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 144 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 120 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 120 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 144 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 144 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 144 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 144 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 144 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 144 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 144 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 144 rows containing missing values or values outside the scale range
## (`geom_line()`).gridExtra::grid.arrange(arranged_plots)
ggsave("Plots/Polymerized040824_Measured041024_plots_DBL.pdf", arranged_plots, width = 16, height = 9, units = "in", device = "pdf")Second Dataset, 4/12/24
Analyze dataset from 4/12/24, for the gels made on 4/10/24. There is an n = 3 for each thickness, with 3 gels of 200 um thickness (“thin”) and 3 gels of 1000 um thickness (“thick”). Three profiles were taken per gel, at a light intensity of PAR 90 umol/m^2/s and a flow rate of 0.5 cm/s. Three additional profiles were taken per gel in the dark after 20 minutes of dark adaptation. The profiles were taken in the middle of the hydrogel, in the same position for all 6 profiles per gel. For this dataset, I began taking profiles into the depth of the gel, which is shown in “positive” depths.
Plotting profiles
files <- list.files(path = "./Polymerized041024_Measured041224", pattern = "\\.csv$", full.names = TRUE)
plot_list <- list()
for (file in files) {
file_name <- tools::file_path_sans_ext(basename(file))
file_data <- read_csv(paste(file),show_col_types = FALSE) %>%
mutate(Replicate = as.factor(Replicate)) %>%
rename("Oxygen (µmol/L)" = `A FireSting Ch1 (µmol/L)`) %>% filter(Condition == "Light" | Condition == "Dark")
plot <- ggplot(data = file_data, aes(y = `Oxygen (µmol/L)`, x = `Depth (µm)`,
color = `Condition`, shape = `Replicate`)) +
geom_point() + geom_line() +
coord_flip() + scale_x_reverse() + xlim(1000, -1450) + ylim(140, 405) +
geom_vline(xintercept = 0, linetype = 3) +
theme_bw() + ggtitle(file_name) +
annotate("text", x = -1400, y = 395, label = paste("Start:", file_data$Time[1]), vjust = 1, hjust = 1, size = 3) +
annotate("text", x = -1300, y = 395, label = paste("End:", file_data$Time[nrow(file_data)]), vjust = 1, hjust = 1, size = 3) +
theme(legend.position = "bottom")
#print(plot)
plot_list[[file_name]] <- plot
}## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`arranged_plots <- arrangeGrob(grobs = plot_list, ncol = 3)## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_line()`).gridExtra::grid.arrange(arranged_plots)
ggsave("Plots/Polymerized041024_Measured041224_plots.pdf", arranged_plots, width = 16, height = 9, units = "in", device = "pdf")Extra profiles taken not plotted above:
files <- c("./Polymerized041024_Measured041224/Gel041024_1000um_1.csv", "./Polymerized041024_Measured041224/Gel041024_200um_3.csv")
plot_list <- list()
for (file in files) {
file_name <- tools::file_path_sans_ext(basename(file))
file_data <- read_csv(paste(file),show_col_types = FALSE) %>%
mutate(Replicate = as.factor(Replicate)) %>%
rename("Oxygen (µmol/L)" = `A FireSting Ch1 (µmol/L)`) %>% filter(Condition != "Light" & Condition != "Dark")
plot <- ggplot(data = file_data, aes(y = `Oxygen (µmol/L)`, x = `Depth (µm)`,
color = `Condition`, shape = `Replicate`)) +
geom_point() + geom_line() +
coord_flip() + scale_x_reverse() + xlim(0, -1450) +
geom_vline(xintercept = 0, linetype = 3) +
theme_bw() + ggtitle(file_name) +
annotate("text", x = -1400, y = 295, label = paste("Start:", file_data$Time[1]), vjust = 1, hjust = 1, size = 3) +
annotate("text", x = -1300, y = 295, label = paste("End:", file_data$Time[nrow(file_data)]), vjust = 1, hjust = 1, size = 3) +
theme(legend.position = "bottom")
#print(plot)
plot_list[[file_name]] <- plot
}## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`arranged_plots <- arrangeGrob(grobs = plot_list, ncol = 3)## Warning: Removed 30 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 30 rows containing missing values or values outside the scale range
## (`geom_line()`).gridExtra::grid.arrange(arranged_plots)
ggsave("Plots/Polymerized041024_Measured041224_plots_extra.pdf", arranged_plots, width = 16, height = 9, units = "in", device = "pdf")Using segmented linear regression, segmented R package
library(segmented)
# Function to perform segmented regression and extract slope after breakpoint
perform_segmented_regression <- function(oxygen_concentration, depth) {
# Fit segmented regression model
fit <- segmented(lm(oxygen_concentration ~ depth), seg.Z = ~depth, psi = -500)
# Extract the breakpoint(s)
breakpoint <- fit$psi[, 2]
# Find the index of the datapoint nearest to the breakpoint
breakpoint_index <- which.min(abs(depth - breakpoint))
# Extract the oxygen concentration at the breakpoint
Oxygen_breakpoint <- oxygen_concentration[breakpoint_index]
# Extract the oxygen concentration at depth = 0
Oxygen_Surface <- oxygen_concentration[which(depth == 0)]
# Extract the slope after the breakpoint for each segment
lm_fit <- lm(oxygen_concentration ~ depth,
data = data.frame(depth = depth[breakpoint_index:length(depth)],
oxygen_concentration = oxygen_concentration[breakpoint_index:length(oxygen_concentration)]))
slope <- coef(lm_fit)["depth"]
# Calculate fitted values for segmented regression lines
fitted_values <- fitted(fit, newdata = data.frame(depth = depth))
# Create dataframe with depth and fitted oxygen concentration values
fitted_data <- data.frame("Depth" = depth, "Oxygen" = fitted_values)
return(list(
slope = slope,
breakpoint = breakpoint,
Oxygen_breakpoint = Oxygen_breakpoint,
Oxygen_Surface = Oxygen_Surface,
fitted_data = fitted_data
))
}
results_df_1000um_segmented <- data.frame(
File = character(),
Scaffold_Thickness = character(),
Replicate = character(),
slope = numeric(),
Flux = numeric(),
Breakpoint = numeric(),
Oxygen_Surface = numeric(),
Oxygen_breakpoint = numeric(),
stringsAsFactors = FALSE
)
# Get list of CSV file names with "1000" in the title
file_list <- list.files(path = "./Polymerized041024_Measured041224", pattern = "1000.*\\.csv$", full.names = TRUE)
# Loop over files
for (file_path in file_list) {
# Read data from file
data <- read_csv(file_path, show_col_types = FALSE) %>%
mutate(Replicate = as.factor(Replicate)) %>%
rename("Oxygen (µmol/L)" = `A FireSting Ch1 (µmol/L)`) %>%
arrange(`Depth (µm)`)
# Loop over replicates
for (replicate_id in unique(data$Replicate)) {
# Subset data for the current replicate
replicate_data <- data %>%
filter(Replicate == replicate_id & Condition == "Light") %>%
filter(`Depth (µm)` <= 0)
# Extract oxygen concentration and depth
oxygen_concentration <- replicate_data %>% pull(`Oxygen (µmol/L)`)
depth <- replicate_data %>% pull(`Depth (µm)`)
# Perform segmented regression
regression_results <- perform_segmented_regression(oxygen_concentration, depth)
# Plot data and segmented regression lines
p <- ggplot(replicate_data, aes(x = `Depth (µm)`, y = `Oxygen (µmol/L)`)) +
geom_point() +
geom_line(data = regression_results$fitted_data, aes(x = `Depth`, y = `Oxygen`), color = "red") +
labs(title = paste("Replicate", replicate_id), x = "Depth (µm)", y = "Oxygen (µmol/L)")
print(p)
# Calculate flux using Fick’s first law of diffusion
DO2 <- 2.2417e-5 # Diffusion coefficient of oxygen
flux <- -DO2 * regression_results$slope
# Store results in dataframe
results_df_1000um_segmented <- bind_rows(results_df_1000um_segmented, data.frame(
File = basename(file_path),
Scaffold_Thickness = "1000 um",
Replicate = replicate_id,
slope = regression_results$slope,
Flux = flux,
Breakpoint = regression_results$breakpoint,
Oxygen_Surface = regression_results$Oxygen_Surface,
Oxygen_breakpoint = regression_results$Oxygen_breakpoint,
stringsAsFactors = FALSE
))
}
}## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
# Print the results dataframe
print(results_df_1000um_segmented)## File Scaffold_Thickness Replicate slope
## depth...1 Gel041024_1000um_1.csv 1000 um 1 0.1886047
## depth...2 Gel041024_1000um_1.csv 1000 um 2 0.1865547
## depth...3 Gel041024_1000um_1.csv 1000 um 3 0.1909753
## depth...4 Gel041024_1000um_2.csv 1000 um 1 0.1564983
## depth...5 Gel041024_1000um_2.csv 1000 um 2 0.1645147
## depth...6 Gel041024_1000um_2.csv 1000 um 3 0.1813063
## depth...7 Gel041024_1000um_3.csv 1000 um 1 0.2525014
## depth...8 Gel041024_1000um_3.csv 1000 um 2 0.1921323
## depth...9 Gel041024_1000um_3.csv 1000 um 3 0.1993643
## Flux Breakpoint Oxygen_Surface Oxygen_breakpoint
## depth...1 -4.227951e-06 -417.4311 288.860 216.726
## depth...2 -4.181996e-06 -418.5536 289.131 216.760
## depth...3 -4.281094e-06 -389.4811 288.271 215.303
## depth...4 -3.508223e-06 -380.1215 273.166 212.490
## depth...5 -3.687925e-06 -387.7747 275.934 212.737
## depth...6 -4.064344e-06 -412.8095 280.751 211.682
## depth...7 -5.660325e-06 -321.0366 288.669 216.095
## depth...8 -4.307031e-06 -385.5178 287.524 212.723
## depth...9 -4.469150e-06 -385.2196 291.139 213.216results_df_200um_segmented <- data.frame(
File = character(),
Scaffold_Thickness = character(),
Replicate = character(),
slope = numeric(),
Flux = numeric(),
Breakpoint = numeric(),
Oxygen_Surface = numeric(),
Oxygen_breakpoint = numeric(),
stringsAsFactors = FALSE
)
# Get list of CSV file names with "200" in the title
file_list <- list.files(path = "./Polymerized041024_Measured041224", pattern = "200.*\\.csv$", full.names = TRUE)
# Loop over files
for (file_path in file_list) {
# Read data from file
data <- read_csv(file_path, show_col_types = FALSE) %>%
mutate(Replicate = as.factor(Replicate)) %>%
rename("Oxygen (µmol/L)" = `A FireSting Ch1 (µmol/L)`) %>%
arrange(`Depth (µm)`)
# Loop over replicates
for (replicate_id in unique(data$Replicate)) {
# Subset data for the current replicate
replicate_data <- data %>%
filter(Replicate == replicate_id & Condition == "Light") %>%
filter(`Depth (µm)` <= 0)
# Extract oxygen concentration and depth
oxygen_concentration <- replicate_data %>% pull(`Oxygen (µmol/L)`)
depth <- replicate_data %>% pull(`Depth (µm)`)
# Perform segmented regression
regression_results <- perform_segmented_regression(oxygen_concentration, depth)
# Plot data and segmented regression lines
p <- ggplot(replicate_data, aes(x = `Depth (µm)`, y = `Oxygen (µmol/L)`)) +
geom_point() +
geom_line(data = regression_results$fitted_data, aes(x = `Depth`, y = `Oxygen`), color = "red") +
labs(title = paste("Replicate", replicate_id), x = "Depth (µm)", y = "Oxygen (µmol/L)")
print(p)
# Calculate flux using Fick’s first law of diffusion
DO2 <- 2.2417e-5 # Diffusion coefficient of oxygen
flux <- -DO2 * regression_results$slope
# Store results in dataframe
results_df_200um_segmented <- bind_rows(results_df_200um_segmented, data.frame(
File = basename(file_path),
Scaffold_Thickness = "200 um",
Replicate = replicate_id,
slope = regression_results$slope,
Flux = flux,
Breakpoint = regression_results$breakpoint,
Oxygen_Surface = regression_results$Oxygen_Surface,
Oxygen_breakpoint = regression_results$Oxygen_breakpoint,
stringsAsFactors = FALSE
))
}
}## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
# Print the results dataframe
print(results_df_200um_segmented)## File Scaffold_Thickness Replicate slope
## depth...1 Gel041024_200um_1.csv 200 um 1 0.2021880
## depth...2 Gel041024_200um_1.csv 200 um 2 0.2184340
## depth...3 Gel041024_200um_1.csv 200 um 3 0.2466440
## depth...4 Gel041024_200um_2.csv 200 um 1 0.1583019
## depth...5 Gel041024_200um_2.csv 200 um 2 0.1779883
## depth...6 Gel041024_200um_2.csv 200 um 3 0.1804715
## depth...7 Gel041024_200um_3.csv 200 um 1 0.1273800
## depth...8 Gel041024_200um_3.csv 200 um 2 0.1296086
## depth...9 Gel041024_200um_3.csv 200 um 3 0.1346250
## Flux Breakpoint Oxygen_Surface Oxygen_breakpoint
## depth...1 -4.532448e-06 -423.0096 284.485 207.667
## depth...2 -4.896635e-06 -386.3812 289.267 205.874
## depth...3 -5.529019e-06 -385.2487 294.029 202.154
## depth...4 -3.548654e-06 -373.7823 258.055 204.006
## depth...5 -3.989964e-06 -420.7698 272.079 203.448
## depth...6 -4.045630e-06 -438.4452 279.738 201.503
## depth...7 -2.855477e-06 -276.4187 234.767 197.122
## depth...8 -2.905435e-06 -277.3252 235.705 198.117
## depth...9 -3.017889e-06 -278.9906 236.974 197.286Full_results_segmented <- rbind(results_df_1000um_segmented, results_df_200um_segmented)
Full_results_segmented## File Scaffold_Thickness Replicate slope
## depth...1 Gel041024_1000um_1.csv 1000 um 1 0.1886047
## depth...2 Gel041024_1000um_1.csv 1000 um 2 0.1865547
## depth...3 Gel041024_1000um_1.csv 1000 um 3 0.1909753
## depth...4 Gel041024_1000um_2.csv 1000 um 1 0.1564983
## depth...5 Gel041024_1000um_2.csv 1000 um 2 0.1645147
## depth...6 Gel041024_1000um_2.csv 1000 um 3 0.1813063
## depth...7 Gel041024_1000um_3.csv 1000 um 1 0.2525014
## depth...8 Gel041024_1000um_3.csv 1000 um 2 0.1921323
## depth...9 Gel041024_1000um_3.csv 1000 um 3 0.1993643
## depth...11 Gel041024_200um_1.csv 200 um 1 0.2021880
## depth...21 Gel041024_200um_1.csv 200 um 2 0.2184340
## depth...31 Gel041024_200um_1.csv 200 um 3 0.2466440
## depth...41 Gel041024_200um_2.csv 200 um 1 0.1583019
## depth...51 Gel041024_200um_2.csv 200 um 2 0.1779883
## depth...61 Gel041024_200um_2.csv 200 um 3 0.1804715
## depth...71 Gel041024_200um_3.csv 200 um 1 0.1273800
## depth...81 Gel041024_200um_3.csv 200 um 2 0.1296086
## depth...91 Gel041024_200um_3.csv 200 um 3 0.1346250
## Flux Breakpoint Oxygen_Surface Oxygen_breakpoint
## depth...1 -4.227951e-06 -417.4311 288.860 216.726
## depth...2 -4.181996e-06 -418.5536 289.131 216.760
## depth...3 -4.281094e-06 -389.4811 288.271 215.303
## depth...4 -3.508223e-06 -380.1215 273.166 212.490
## depth...5 -3.687925e-06 -387.7747 275.934 212.737
## depth...6 -4.064344e-06 -412.8095 280.751 211.682
## depth...7 -5.660325e-06 -321.0366 288.669 216.095
## depth...8 -4.307031e-06 -385.5178 287.524 212.723
## depth...9 -4.469150e-06 -385.2196 291.139 213.216
## depth...11 -4.532448e-06 -423.0096 284.485 207.667
## depth...21 -4.896635e-06 -386.3812 289.267 205.874
## depth...31 -5.529019e-06 -385.2487 294.029 202.154
## depth...41 -3.548654e-06 -373.7823 258.055 204.006
## depth...51 -3.989964e-06 -420.7698 272.079 203.448
## depth...61 -4.045630e-06 -438.4452 279.738 201.503
## depth...71 -2.855477e-06 -276.4187 234.767 197.122
## depth...81 -2.905435e-06 -277.3252 235.705 198.117
## depth...91 -3.017889e-06 -278.9906 236.974 197.286mean_flux <- Full_results_segmented %>%
group_by(Scaffold_Thickness) %>%
summarize(mean_flux = mean(Flux))
# Plotting the flux values for each thickness
flux_plot <- ggplot(Full_results_segmented, aes(x = Scaffold_Thickness, y = Flux)) +
geom_boxplot() + geom_point(aes(color = File), position = position_jitter(width = 0.05)) +
geom_text(data = mean_flux, aes(label = paste("Mean Flux = \n", sprintf("%.2e", mean_flux)), y = mean_flux-.000002), vjust = 0.1, position = position_nudge(x = 0.25)) +
labs(x = "Thickness (µm)", y = "Flux") +
ggtitle("Flux Comparison between Thicknesses,\n same areal density (4/12/24)")
flux_plot
ggsave("Plots/Polymerized041024_Measured041224_Flux_plot.pdf", flux_plot, device = "pdf")## Saving 7 x 5 in image# Running ANOVA
anova_result <- aov(Flux ~ Scaffold_Thickness, data = Full_results_segmented)
summary(anova_result)## Df Sum Sq Mean Sq F value Pr(>F)
## Scaffold_Thickness 1 5.230e-13 5.225e-13 0.836 0.374
## Residuals 16 9.997e-12 6.248e-13# What about for oxygen surface concentration?
mean_Oxygen_Surface <- Full_results_segmented %>%
group_by(Scaffold_Thickness) %>%
summarize(mean_Oxygen_Surface = mean(Oxygen_Surface))
# Plotting the surface O2 values for each thickness
ggplot(Full_results_segmented, aes(x = Scaffold_Thickness, y = Oxygen_Surface)) +
geom_boxplot() + geom_point(aes(color = File), position = position_jitter(width = 0.05)) +
geom_text(data = mean_Oxygen_Surface, aes(label = paste("Mean Surface Oxygen = \n", round(mean_Oxygen_Surface, 1)), y = mean_Oxygen_Surface-25), vjust = 0.1, position = position_nudge(x = 0.0)) +
labs(x = "Thickness (µm)", y = "Oxygen Surface Concentration (µmol/L)") +
ggtitle("Oxygen Surface Concentration (µmol/L) Comparison between Thicknesses")
# Running ANOVA
anova_result <- aov(Oxygen_Surface ~ Scaffold_Thickness, data = Full_results_segmented)
summary(anova_result)## Df Sum Sq Mean Sq F value Pr(>F)
## Scaffold_Thickness 1 1767 1767 5.627 0.0306 *
## Residuals 16 5025 314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plotting profiles with DBL
files <- list.files(path = "./Polymerized041024_Measured041224", pattern = "\\.csv$", full.names = TRUE)
plot_list <- list()
for (file in files) {
file_name <- tools::file_path_sans_ext(basename(file))
file_data <- read_csv(paste(file),show_col_types = FALSE) %>%
mutate(Replicate = as.factor(Replicate)) %>%
rename("Oxygen (µmol/L)" = `A FireSting Ch1 (µmol/L)`) %>% filter(Condition == "Light" | Condition == "Dark")
# Extract the DBL depth for the current file
dbl_depth <- Full_results_segmented$Breakpoint[gsub("\\.csv$", "", Full_results_segmented$File) == file_name]
plot <- ggplot(data = file_data, aes(y = `Oxygen (µmol/L)`, x = `Depth (µm)`,
color = `Condition`, shape = `Replicate`)) +
geom_point() + geom_line() +
coord_flip() + scale_x_reverse() + xlim(0, -1000) + ylim(140, 405) +
theme_bw() + ggtitle(file_name) +
annotate("text", x = -900, y = 395, label = paste("Start:", file_data$Time[1]), vjust = 1, hjust = 1, size = 3) +
annotate("text", x = -850, y = 395, label = paste("End:", file_data$Time[nrow(file_data)]), vjust = 1, hjust = 1, size = 3) +
geom_vline(xintercept = dbl_depth, linetype = "dashed") + # Add DBL depth line
theme(legend.position = "bottom")
#print(plot)
plot_list[[file_name]] <- plot
}## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`arranged_plots <- arrangeGrob(grobs = plot_list, ncol = 3)## Warning: Removed 159 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 159 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 156 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 156 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 156 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 156 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 60 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 60 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 66 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 66 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 66 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 66 rows containing missing values or values outside the scale range
## (`geom_line()`).gridExtra::grid.arrange(arranged_plots)
ggsave("Plots/Polymerized041024_Measured041224_plots_DBL.pdf", arranged_plots, width = 16, height = 9, units = "in", device = "pdf")Third Dataset, 4/13/24
Analyze dataset from 4/13/24, for the gels (numbered 1-3) made on 4/08/24. These gels were stressed out using high light (PAR 630 umol/m^2/s) for two hours from 9AM-11AM in the morning in the incubator they were kept in and then held at ambient light (PAR = 90) until profiles were taken. There is an n = 3 for each thickness, with 3 gels of 200 um thickness (“thin”) and 3 gels of 1000 um thickness (“thick”). Three profiles were taken per gel, at a light intensity of PAR 90 umol/m^2/s and a flow rate of 0.5 cm/s. Three additional profiles were taken per gel in high light (PAR 630 umol/m^2/s) after 5-10 minutes of adaptation to equilibrium based on the oxygen surface concentration no longer increasing and reaching steady state. The profiles were taken in the middle of the hydrogel, in the same position for all 6 profiles per gel. Profiles were taken partially into the depth of the gel, which is shown in “positive” depths.
Plotting profiles
files <- list.files(path = "./Polymerized040824_Measured041324", pattern = "\\.csv$", full.names = TRUE)
plot_list <- list()
for (file in files) {
file_name <- tools::file_path_sans_ext(basename(file))
file_data <- read_csv(paste(file),show_col_types = FALSE) %>%
mutate(Replicate = as.factor(Replicate)) %>%
rename("Oxygen (µmol/L)" = `A FireSting Ch1 (µmol/L)`) #%>% filter(Condition == "Light" | Condition == "Dark")
plot <- ggplot(data = file_data, aes(y = `Oxygen (µmol/L)`, x = `Depth (µm)`,
color = `Condition`, shape = `Replicate`)) +
geom_point() + geom_line() +
coord_flip() + scale_x_reverse() + xlim(1000, -1450) + ylim(200, 900) +
geom_vline(xintercept = 0, linetype = 3) +
theme_bw() + ggtitle(file_name) +
annotate("text", x = -1400, y = 395, label = paste("Start:", file_data$Time[1]), vjust = 1, hjust = 1, size = 3) +
annotate("text", x = -1300, y = 395, label = paste("End:", file_data$Time[nrow(file_data)]), vjust = 1, hjust = 1, size = 3) +
theme(legend.position = "bottom")
#print(plot)
plot_list[[file_name]] <- plot
}## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`arranged_plots <- arrangeGrob(grobs = plot_list, ncol = 3)
gridExtra::grid.arrange(arranged_plots)
ggsave("Plots/Polymerized040824_Measured041324_plots.pdf", arranged_plots, width = 16, height = 9, units = "in", device = "pdf")Using segmented linear regression, segmented R package
library(segmented)
# Function to perform segmented regression and extract slope after breakpoint
perform_segmented_regression <- function(oxygen_concentration, depth) {
# Fit segmented regression model
fit <- segmented(lm(oxygen_concentration ~ depth), seg.Z = ~depth, psi = -250)
# Extract the breakpoint(s)
breakpoint <- fit$psi[, 2]
# Find the index of the datapoint nearest to the breakpoint
breakpoint_index <- which.min(abs(depth - breakpoint))
# Extract the oxygen concentration at the breakpoint
Oxygen_breakpoint <- oxygen_concentration[breakpoint_index]
# Extract the oxygen concentration at depth = 0
Oxygen_Surface <- oxygen_concentration[which(depth == 0)]
# Extract the slope after the breakpoint for each segment
lm_fit <- lm(oxygen_concentration ~ depth,
data = data.frame(depth = depth[breakpoint_index:length(depth)],
oxygen_concentration = oxygen_concentration[breakpoint_index:length(oxygen_concentration)]))
slope <- coef(lm_fit)["depth"]
# Calculate fitted values for segmented regression lines
fitted_values <- fitted(fit, newdata = data.frame(depth = depth))
# Create dataframe with depth and fitted oxygen concentration values
fitted_data <- data.frame("Depth" = depth, "Oxygen" = fitted_values)
return(list(
slope = slope,
breakpoint = breakpoint,
Oxygen_breakpoint = Oxygen_breakpoint,
Oxygen_Surface = Oxygen_Surface,
fitted_data = fitted_data
))
}
results_df_1000um_segmented <- data.frame(
File = character(),
Scaffold_Thickness = character(),
Replicate = character(),
slope = numeric(),
Flux = numeric(),
Breakpoint = numeric(),
Oxygen_Surface = numeric(),
Oxygen_breakpoint = numeric(),
stringsAsFactors = FALSE
)
# Get list of CSV file names with "1000" in the title
file_list <- list.files(path = "./Polymerized040824_Measured041324", pattern = "1000.*\\.csv$", full.names = TRUE)
# Loop over files
for (file_path in file_list) {
# Read data from file
data <- read_csv(file_path, show_col_types = FALSE) %>%
mutate(Replicate = as.factor(Replicate)) %>%
rename("Oxygen (µmol/L)" = `A FireSting Ch1 (µmol/L)`) %>%
arrange(`Depth (µm)`)
# Loop over replicates
for (replicate_id in unique(data$Replicate)) {
# Subset data for the current replicate
replicate_data <- data %>%
filter(Replicate == replicate_id & Condition == "Light") %>%
filter(`Depth (µm)` <= 0)
# Extract oxygen concentration and depth
oxygen_concentration <- replicate_data %>% pull(`Oxygen (µmol/L)`)
depth <- replicate_data %>% pull(`Depth (µm)`)
# Perform segmented regression
regression_results <- perform_segmented_regression(oxygen_concentration, depth)
# Plot data and segmented regression lines
p <- ggplot(replicate_data, aes(x = `Depth (µm)`, y = `Oxygen (µmol/L)`)) +
geom_point() +
geom_line(data = regression_results$fitted_data, aes(x = `Depth`, y = `Oxygen`), color = "red") +
labs(title = paste("Replicate", replicate_id), x = "Depth (µm)", y = "Oxygen (µmol/L)")
print(p)
# Calculate flux using Fick’s first law of diffusion
DO2 <- 2.2417e-5 # Diffusion coefficient of oxygen
flux <- -DO2 * regression_results$slope
# Store results in dataframe
results_df_1000um_segmented <- bind_rows(results_df_1000um_segmented, data.frame(
File = basename(file_path),
Scaffold_Thickness = "1000 um",
Replicate = replicate_id,
slope = regression_results$slope,
Flux = flux,
Breakpoint = regression_results$breakpoint,
Oxygen_Surface = regression_results$Oxygen_Surface,
Oxygen_breakpoint = regression_results$Oxygen_breakpoint,
stringsAsFactors = FALSE
))
}
}## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
# Print the results dataframe
print(results_df_1000um_segmented)## File Scaffold_Thickness Replicate slope
## depth...1 Gel040824_1000um_1.csv 1000 um 1 0.5821080
## depth...2 Gel040824_1000um_1.csv 1000 um 2 0.5695080
## depth...3 Gel040824_1000um_1.csv 1000 um 3 0.5511640
## depth...4 Gel040824_1000um_2.csv 1000 um 1 0.5025440
## depth...5 Gel040824_1000um_2.csv 1000 um 2 0.4722500
## depth...6 Gel040824_1000um_2.csv 1000 um 3 0.4822000
## depth...7 Gel040824_1000um_3.csv 1000 um 1 0.3841909
## depth...8 Gel040824_1000um_3.csv 1000 um 2 0.4481669
## depth...9 Gel040824_1000um_3.csv 1000 um 3 0.4936017
## Flux Breakpoint Oxygen_Surface Oxygen_breakpoint
## depth...1 -1.304912e-05 -222.7569 350.297 235.901
## depth...2 -1.276666e-05 -214.8453 342.694 230.712
## depth...3 -1.235544e-05 -239.9065 360.905 227.053
## depth...4 -1.126553e-05 -223.8684 327.998 231.279
## depth...5 -1.058643e-05 -217.2484 317.507 225.020
## depth...6 -1.080948e-05 -214.3318 318.232 223.906
## depth...7 -8.612406e-06 -234.9614 312.305 218.707
## depth...8 -1.004656e-05 -231.9015 328.562 218.860
## depth...9 -1.106507e-05 -229.3515 341.538 220.488results_df_200um_segmented <- data.frame(
File = character(),
Scaffold_Thickness = character(),
Replicate = character(),
slope = numeric(),
Flux = numeric(),
Breakpoint = numeric(),
Oxygen_Surface = numeric(),
Oxygen_breakpoint = numeric(),
stringsAsFactors = FALSE
)
# Get list of CSV file names with "200" in the title
file_list <- list.files(path = "./Polymerized040824_Measured041324", pattern = "200.*\\.csv$", full.names = TRUE)
# Loop over files
for (file_path in file_list) {
# Read data from file
data <- read_csv(file_path, show_col_types = FALSE) %>%
mutate(Replicate = as.factor(Replicate)) %>%
rename("Oxygen (µmol/L)" = `A FireSting Ch1 (µmol/L)`) %>%
arrange(`Depth (µm)`)
# Loop over replicates
for (replicate_id in unique(data$Replicate)) {
# Subset data for the current replicate
replicate_data <- data %>%
filter(Replicate == replicate_id & Condition == "Light") %>%
filter(`Depth (µm)` <= 0)
# Extract oxygen concentration and depth
oxygen_concentration <- replicate_data %>% pull(`Oxygen (µmol/L)`)
depth <- replicate_data %>% pull(`Depth (µm)`)
# Perform segmented regression
regression_results <- perform_segmented_regression(oxygen_concentration, depth)
# Plot data and segmented regression lines
p <- ggplot(replicate_data, aes(x = `Depth (µm)`, y = `Oxygen (µmol/L)`)) +
geom_point() +
geom_line(data = regression_results$fitted_data, aes(x = `Depth`, y = `Oxygen`), color = "red") +
labs(title = paste("Replicate", replicate_id), x = "Depth (µm)", y = "Oxygen (µmol/L)")
print(p)
# Calculate flux using Fick’s first law of diffusion
DO2 <- 2.2417e-5 # Diffusion coefficient of oxygen
flux <- -DO2 * regression_results$slope
# Store results in dataframe
results_df_200um_segmented <- bind_rows(results_df_200um_segmented, data.frame(
File = basename(file_path),
Scaffold_Thickness = "200 um",
Replicate = replicate_id,
slope = regression_results$slope,
Flux = flux,
Breakpoint = regression_results$breakpoint,
Oxygen_Surface = regression_results$Oxygen_Surface,
Oxygen_breakpoint = regression_results$Oxygen_breakpoint,
stringsAsFactors = FALSE
))
}
}## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
## New names:
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`
# Print the results dataframe
print(results_df_200um_segmented)## File Scaffold_Thickness Replicate slope
## depth...1 Gel040824_200um_1.csv 200 um 1 0.2072080
## depth...2 Gel040824_200um_1.csv 200 um 2 0.2168434
## depth...3 Gel040824_200um_1.csv 200 um 3 0.2427029
## depth...4 Gel040824_200um_2.csv 200 um 1 0.2040657
## depth...5 Gel040824_200um_2.csv 200 um 2 0.2093743
## depth...6 Gel040824_200um_2.csv 200 um 3 0.2081051
## depth...7 Gel040824_200um_3.csv 200 um 1 0.2263164
## depth...8 Gel040824_200um_3.csv 200 um 2 0.2147954
## depth...9 Gel040824_200um_3.csv 200 um 3 0.2215040
## Flux Breakpoint Oxygen_Surface Oxygen_breakpoint
## depth...1 -4.644982e-06 -232.9567 265.988 215.260
## depth...2 -4.860979e-06 -229.2693 267.437 214.600
## depth...3 -5.440670e-06 -267.6632 276.688 217.204
## depth...4 -4.574541e-06 -271.7906 265.946 215.835
## depth...5 -4.693543e-06 -268.1471 266.835 215.400
## depth...6 -4.665093e-06 -262.7707 264.796 213.796
## depth...7 -5.073335e-06 -319.0819 282.498 216.533
## depth...8 -4.815069e-06 -236.1913 265.580 213.431
## depth...9 -4.965455e-06 -224.9881 260.208 216.236Full_results_segmented <- rbind(results_df_1000um_segmented, results_df_200um_segmented)
Full_results_segmented## File Scaffold_Thickness Replicate slope
## depth...1 Gel040824_1000um_1.csv 1000 um 1 0.5821080
## depth...2 Gel040824_1000um_1.csv 1000 um 2 0.5695080
## depth...3 Gel040824_1000um_1.csv 1000 um 3 0.5511640
## depth...4 Gel040824_1000um_2.csv 1000 um 1 0.5025440
## depth...5 Gel040824_1000um_2.csv 1000 um 2 0.4722500
## depth...6 Gel040824_1000um_2.csv 1000 um 3 0.4822000
## depth...7 Gel040824_1000um_3.csv 1000 um 1 0.3841909
## depth...8 Gel040824_1000um_3.csv 1000 um 2 0.4481669
## depth...9 Gel040824_1000um_3.csv 1000 um 3 0.4936017
## depth...11 Gel040824_200um_1.csv 200 um 1 0.2072080
## depth...21 Gel040824_200um_1.csv 200 um 2 0.2168434
## depth...31 Gel040824_200um_1.csv 200 um 3 0.2427029
## depth...41 Gel040824_200um_2.csv 200 um 1 0.2040657
## depth...51 Gel040824_200um_2.csv 200 um 2 0.2093743
## depth...61 Gel040824_200um_2.csv 200 um 3 0.2081051
## depth...71 Gel040824_200um_3.csv 200 um 1 0.2263164
## depth...81 Gel040824_200um_3.csv 200 um 2 0.2147954
## depth...91 Gel040824_200um_3.csv 200 um 3 0.2215040
## Flux Breakpoint Oxygen_Surface Oxygen_breakpoint
## depth...1 -1.304912e-05 -222.7569 350.297 235.901
## depth...2 -1.276666e-05 -214.8453 342.694 230.712
## depth...3 -1.235544e-05 -239.9065 360.905 227.053
## depth...4 -1.126553e-05 -223.8684 327.998 231.279
## depth...5 -1.058643e-05 -217.2484 317.507 225.020
## depth...6 -1.080948e-05 -214.3318 318.232 223.906
## depth...7 -8.612406e-06 -234.9614 312.305 218.707
## depth...8 -1.004656e-05 -231.9015 328.562 218.860
## depth...9 -1.106507e-05 -229.3515 341.538 220.488
## depth...11 -4.644982e-06 -232.9567 265.988 215.260
## depth...21 -4.860979e-06 -229.2693 267.437 214.600
## depth...31 -5.440670e-06 -267.6632 276.688 217.204
## depth...41 -4.574541e-06 -271.7906 265.946 215.835
## depth...51 -4.693543e-06 -268.1471 266.835 215.400
## depth...61 -4.665093e-06 -262.7707 264.796 213.796
## depth...71 -5.073335e-06 -319.0819 282.498 216.533
## depth...81 -4.815069e-06 -236.1913 265.580 213.431
## depth...91 -4.965455e-06 -224.9881 260.208 216.236mean_flux <- Full_results_segmented %>%
group_by(Scaffold_Thickness) %>%
summarize(mean_flux = mean(Flux))
# Plotting the flux values for each thickness
flux_plot <- ggplot(Full_results_segmented, aes(x = Scaffold_Thickness, y = Flux)) +
geom_boxplot() + geom_point(aes(color = File,shape = Replicate), position = position_jitter(width = 0.05)) +
geom_text(data = mean_flux, aes(label = paste("Mean Flux = \n", sprintf("%.2e", mean_flux)), y = mean_flux-.000002), vjust = 0.1, position = position_nudge(x = 0.25)) +
labs(x = "Thickness (µm)", y = "Flux") +
ggtitle("Flux Comparison between Thicknesses,\n same volumetric density (4/13/24)")
flux_plot
ggsave("Plots/Polymerized040824_Measured041324_Flux_plot.pdf", flux_plot, device = "pdf")## Saving 7 x 5 in image# Running ANOVA
anova_result <- aov(Flux ~ Scaffold_Thickness, data = Full_results_segmented)
summary(anova_result)## Df Sum Sq Mean Sq F value Pr(>F)
## Scaffold_Thickness 1 1.794e-10 1.794e-10 175.3 4.88e-10 ***
## Residuals 16 1.637e-11 1.020e-12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# What about for oxygen surface concentration?
mean_Oxygen_Surface <- Full_results_segmented %>%
group_by(Scaffold_Thickness) %>%
summarize(mean_Oxygen_Surface = mean(Oxygen_Surface))
# Plotting the surface O2 values for each thickness
ggplot(Full_results_segmented, aes(x = Scaffold_Thickness, y = Oxygen_Surface)) +
geom_boxplot() + geom_point(aes(color = File), position = position_jitter(width = 0.05)) +
geom_text(data = mean_Oxygen_Surface, aes(label = paste("Mean Surface Oxygen = \n", round(mean_Oxygen_Surface, 1)), y = mean_Oxygen_Surface-25), vjust = 0.1, position = position_nudge(x = 0.0)) +
labs(x = "Thickness (µm)", y = "Oxygen Surface Concentration (µmol/L)") +
ggtitle("Oxygen Surface Concentration (µmol/L) Comparison between Thicknesses")
# Running ANOVA
anova_result <- aov(Oxygen_Surface ~ Scaffold_Thickness, data = Full_results_segmented)
summary(anova_result)## Df Sum Sq Mean Sq F value Pr(>F)
## Scaffold_Thickness 1 18952 18952 119.1 8.03e-09 ***
## Residuals 16 2546 159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plotting profiles with DBL
files <- list.files(path = "./Polymerized040824_Measured041324", pattern = "\\.csv$", full.names = TRUE)
plot_list <- list()
for (file in files) {
file_name <- tools::file_path_sans_ext(basename(file))
file_data <- read_csv(paste(file),show_col_types = FALSE) %>%
mutate(Replicate = as.factor(Replicate)) %>%
rename("Oxygen (µmol/L)" = `A FireSting Ch1 (µmol/L)`) %>% filter(Condition == "Light" | Condition == "Dark")
# Extract the DBL depth for the current file
dbl_depth <- Full_results_segmented$Breakpoint[gsub("\\.csv$", "", Full_results_segmented$File) == file_name]
plot <- ggplot(data = file_data, aes(y = `Oxygen (µmol/L)`, x = `Depth (µm)`,
color = `Condition`, shape = `Replicate`)) +
geom_point() + geom_line() +
coord_flip() + scale_x_reverse() + xlim(0, -1000) + ylim(200, 400) +
theme_bw() + ggtitle(file_name) +
annotate("text", x = -900, y = 395, label = paste("Start:", file_data$Time[1]), vjust = 1, hjust = 1, size = 3) +
annotate("text", x = -850, y = 395, label = paste("End:", file_data$Time[nrow(file_data)]), vjust = 1, hjust = 1, size = 3) +
geom_vline(xintercept = dbl_depth, linetype = "dashed") + # Add DBL depth line
theme(legend.position = "bottom")
#print(plot)
plot_list[[file_name]] <- plot
}## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## New names:
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
## • `B No sensor` -> `B No sensor...7`
## • `B No sensor` -> `B No sensor...9`arranged_plots <- arrangeGrob(grobs = plot_list, ncol = 3)## Warning: Removed 72 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 72 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 72 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 72 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 72 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 72 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 33 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 33 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 33 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 33 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 33 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 33 rows containing missing values or values outside the scale range
## (`geom_line()`).gridExtra::grid.arrange(arranged_plots)
ggsave("Plots/Polymerized040824_Measured041324_plots_DBL.pdf", arranged_plots, width = 16, height = 9, units = "in", device = "pdf")